use UMAS0007_OUTPUT.DTA, clear

svyset [pw=weight]

recode Q1_1-Q1_10 (.=9)

* Approval
svy: tab Q22_a
svy: tab Q22_b
svy: tab Q22_e
svy: tab Q22_f

* Ranking of issues
svy: tab Q1_1
svy: tab Q1_2
svy: tab Q1_3
svy: tab Q1_4
svy: tab Q1_5
svy: tab Q1_6
svy: tab Q1_7
svy: tab Q1_8
svy: tab Q1_9
svy: tab Q1_10

recode Q2_cents_d Q3_cents_d (.=0)
gen minwagecurrent=Q2_dollars_d+(Q2_cents_d/100)
gen minwagepref=Q3_dollars_d+(Q3_cents_d/100)

twoway histogram minwagecurrent, scheme(s1color) bc(blue) discrete percent xlabel(0 5 "$5" 7.25 "$7.25" 10 "$10" 15 "$15" 20 "$20") text(25 7.25 "Correct answer", size(vsmall)) xtitle("How much is federal minimum wage?") aspect(.2) 
twoway histogram minwagepref, scheme(s1color) bc(blue) discrete percent xlabel(0 5 "$5" 7.25 "$7.25" 10 "$10" 15 "$15" 20 "$20") xtitle("How much should federal minimum wage be?") aspect(.2)

* Voting lines
svy: tab Q15 Q15rand, col  stubw(50)

* 

* Gun control
svy: tab Q8 Q8rand, col stubw(50)

* Immigration

svy: tab Q7_a
svy: tab Q7_b
svy: tab Q7_c
svy: tab Q7_d
svy: tab Q7_e

*******************EVERYTHING AFTER THIS POINT IS ADDED BY MEL - NOT IN ORIGINAL DO FILE BY BRIAN******************
*ideology 3 point
gen ideo3=ideo5
lab var ideo3 "ideology 3 point"
recode ideo3 1/2=1 3=2 4/5=3 6=.
label define ideo3label 1 "liberal" 2 "moderate" 3 "conservative"
label val ideo3 ideo3label

*AGE
gen age=2013-birthyr
gen agecat=1 if age<30
replace agecat=2 if age>29 & age<55
replace agecat=3 if age>54

label define agecat 1 "18-29" 2 "30-54" 3 "55+"
label values agecat agecat

*REGION
gen region=1 if inputstate==9 | inputstate==23 | inputstate==25 | inputstate==33 | inputstate==44 | inputstate==50 | inputstate==34 | inputstate==36 | inputstate==42 
replace region=2 if inputstate==18 | inputstate==17 | inputstate==26 | inputstate==39 | inputstate==55 | inputstate==19 | inputstate==20 | inputstate==27 | inputstate==29 | inputstate==31 | inputstate==38 | inputstate==46
replace region=3 if inputstate==10 | inputstate==11 | inputstate==12 | inputstate==13 | inputstate==24 | inputstate==37 | inputstate==45 | inputstate==51 | inputstate==54 | inputstate==1 | inputstate==21 | inputstate==28 | inputstate==47 | inputstate==5 | inputstate==22 | inputstate==40 | inputstate==48 
replace region=4 if inputstate==4 | inputstate==8 | inputstate==16 | inputstate==35 | inputstate==30 | inputstate==49 | inputstate==32 | inputstate==56 | inputstate==2 | inputstate==6 | inputstate==15 | inputstate==41 | inputstate==53
label var region "region"
label define regionlabel 1 "Northeast" 2 "Midwest" 3 "South" 4 "West"
label val region regionlabel

*BORDER STATES
gen borderstate=1 if inputstate==12 | inputstate==48 | inputstate==4 | inputstate==35 | inputstate==6
recode borderstate .=0
label define bslabel 1 "Border state" 0 "Non-border state"
label val borderstate bslabel


* INCOME CAT
gen incomecat=1 if faminc==1 | faminc==2 | faminc==3 | faminc==4 | faminc==5
replace incomecat=2 if faminc==6 | faminc==7 | faminc==8 | faminc==9
replace incomecat=3 if faminc==10 | faminc==11 | faminc==12 | faminc==13 | faminc==14 | faminc==15 | faminc==16
label var incomecat "income categories (3)"
label define incomelabel 1 "under $50k" 2 "$50k-100k" 3 "$100k or more"
label val incomecat incomelabel

* RETIREMENT AGE BY PROFESSION

*Construction
svy: mean Q11_1_1
*Retail and sales
svy: mean Q11_2_1
*Office clerk
svy: mean Q11_3_1
*Teaching
svy: mean Q11_4_1
*Food preparation and service
svy: mean Q11_5_1
*Nurses
svy: mean Q11_6_1
*Executives and managers
svy: mean Q11_7_1


*VOTING RIGHTS

*Voting Rights Act
svy: tab Q16

*Voting Rights Act by Race
svy: tab Q16 race, col stubw(50)

*Voting Rights Act by Party
svy: tab Q16 pid3, col stubw(50)

*Voting Rights Act by Ideo
svy: tab Q16 ideo3, col stubw(50)

*Voting Rights by Gender
svy: tab Q16 gender, col stubw(50)

*Voting Rights Act by Region
svy: tab Q16 region, col stubw(50)

*Voting Rights Act by Age
svy: tab Q16 agecat, col stubw(50)

*Voting Rights Act by Income
svy: tab Q16 incomecat, col stubw(50)

*VOTING LINES

* Voting lines
svy: tab Q15 Q15rand, col  stubw(50)

* Voting Lines and Race
*B Pic
svy: tab Q15 race if Q15rand==1, col  stubw(50)
*W Pic
svy: tab Q15 race if Q15rand==2, col  stubw(50)
*No Pic
svy: tab Q15 race if Q15rand==3, col  stubw(50)

*Voting Lines and Gender
*B Pic
svy: tab Q15 gender if Q15rand==1, col  stubw(50)
*W Pic
svy: tab Q15 gender if Q15rand==2, col  stubw(50)
*No Pic
svy: tab Q15 gender if Q15rand==3, col  stubw(50)

*Voting Lines and Party
*B Pic
svy: tab Q15 pid3 if Q15rand==1, col  stubw(50)
*W Pic
svy: tab Q15 pid3 if Q15rand==2, col  stubw(50)
*No Pic
svy: tab Q15 pid3 if Q15rand==3, col  stubw(50)

*Voting Lines and Ideo
*B Pic
svy: tab Q15 ideo3 if Q15rand==1, col  stubw(50)
*W Pic
svy: tab Q15 ideo3 if Q15rand==2, col  stubw(50)
*No Pic
svy: tab Q15 ideo3 if Q15rand==3, col  stubw(50)

*Voting Lines and Age
*B Pic
svy: tab Q15 agecat if Q15rand==1, col  stubw(50)
*W Pic
svy: tab Q15 agecat if Q15rand==2, col  stubw(50)
*No Pic
svy: tab Q15 agecat if Q15rand==3, col  stubw(50)


*GUN CONTROL WITH PICTURE

* Gun control
svy: tab Q8 Q8rand, col stubw(50)

*Gun Control by Party
*Pic
svy: tab Q8 pid3 if Q8rand==1, col stubw(50)
*No Pic
svy: tab Q8 pid3 if Q8rand==2, col stubw(50)

*Gun Control by Ideo
*Pic
svy: tab Q8 ideo3 if Q8rand==1, col stubw(50)
*No Pic
svy: tab Q8 ideo3 if Q8rand==2, col stubw(50)

*Gun Control by Gender
*Pic
svy: tab Q8 gender if Q8rand==1, col stubw(50)
*No Pic
svy: tab Q8 gender if Q8rand==2, col stubw(50)

*Gun Control by Race
*Pic
svy: tab Q8 race if Q8rand==1, col stubw(50)
*No Pic
svy: tab Q8 race if Q8rand==2, col stubw(50)


*APPROVAL/FAVORABILITY

*Obama:

*Obama by Party
svy: tab Q22_a pid3, col stubw(50)

*Obama by Race
svy: tab Q22_a race, col stubw(50)

*Obama by Gender
svy: tab Q22_a gender, col stubw(50)

*Obama by Race and Gender

*Obama by Race and Gender Men
svy: tab Q22_a race if gender==1, col stubw(50)
*Obama by Race and Gender Women
svy: tab Q22_a race if gender==2, col stubw(50)

*Obama by Ideo
svy: tab Q22_a ideo3, col stubw(50)

*Obama by Age
svy: tab Q22_a agecat, col stubw(50)

*Obama by Income
svy: tab Q22_a incomecat, col stubw(50)

*Obama by Marriage Status
svy: tab Q22_a marstat, col stubw(50)
*Obama by Marriage Status Men
svy: tab Q22_a marstat if gender==1, col stubw(50)
*Obama by Marriage Status Women
svy: tab Q22_a marstat if gender==2, col stubw(50)

* Biden:
svy: tab Q22_b

*Biden by Party
svy: tab Q22_b pid3, col stubw(50)

* Biden by Race
svy: tab Q22_b race, col stubw(50)

* Biden by Gender
svy: tab Q22_b gender, col stubw(50)

* Biden by Race and Gender

* Biden by Race and Gender Men
svy: tab Q22_b race if gender==1, col stubw(50)
* Biden by Race and Gender Women
svy: tab Q22_b race if gender==2, col stubw(50)

* Biden by Ideo
svy: tab Q22_b ideo3, col stubw(50)

*Biden by Age
svy: tab Q22_b agecat, col stubw(50)

*Biden by Income
svy: tab Q22_b incomecat, col stubw(50)

* Biden by Marriage Status
svy: tab Q22_b marstat, col stubw(50)
* Biden by Marriage Status Men
svy: tab Q22_b marstat if gender==1, col stubw(50)
* Biden by Marriage Status Women
svy: tab Q22_b marstat if gender==2, col stubw(50)

* Kerry:
svy: tab Q22_e

* Kerry by Party
svy: tab Q22_e pid3, col stubw(50)

* Kerry by Race
svy: tab Q22_e race, col stubw(50)

* Kerry by Gender
svy: tab Q22_e gender, col stubw(50)

* Kerry by Race and Gender

* Kerry by Race and Gender Men
svy: tab Q22_e race if gender==1, col stubw(50)
* Kerry by Race and Gender Women
svy: tab Q22_e race if gender==2, col stubw(50)

* Kerry by Ideo
svy: tab Q22_e ideo3, col stubw(50)

* Kerry by Employment
svy: tab Q22_e employ, col stubw(50)

*Kerry by Age
svy: tab Q22_e agecat, col stubw(50)

*Kerry by Income
svy: tab Q22_e incomecat, col stubw(50)

* Kerry by Marriage Status
svy: tab Q22_e marstat, col stubw(50)
* Kerry by Marriage Status Men
svy: tab Q22_e marstat if gender==1, col stubw(50)
* Kerry by Marriage Status Women
svy: tab Q22_e marstat if gender==2, col stubw(50)

* Boehner:
svy: tab Q22_f

* Boehner by Party
svy: tab Q22_f pid3, col stubw(50)

* Boehner by Race
svy: tab Q22_f race, col stubw(50)

* Boehner by Gender
svy: tab Q22_f gender, col stubw(50)

* Boehner by Race and Gender

* Boehner by Race and Gender Men
svy: tab Q22_f race if gender==1, col stubw(50)
* Boehner by Race and Gender Women
svy: tab Q22_f race if gender==2, col stubw(50)

* Boehner by Ideo
svy: tab Q22_f ideo3, col stubw(50)

*Boehner by Age
svy: tab Q22_f agecat, col stubw(50)

*Boehner by Income
svy: tab Q22_f incomecat, col stubw(50)

* Boehner by Employment
svy: tab Q22_f employ, col stubw(50)

* Boehner by Marriage Status
svy: tab Q22_f marstat, col stubw(50)
* Boehner by Marriage Status Men
svy: tab Q22_f marstat if gender==1, col stubw(50)
* Boehner by Marriage Status Women
svy: tab Q22_f marstat if gender==2, col stubw(50)


*MINIMUM WAGE CURRENT

*AVERAGE
svy: mean minwagecurrent

*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean minwagecurrent, over(gender)
*Race
svy: mean minwagecurrent, over(race)
*Party ID
svy: mean minwagecurrent, over(pid3)
*Ideology
svy: mean minwagecurrent, over(ideo3)
*Income
svy: mean minwagecurrent, over(incomecat)
*Age Category
svy: mean minwagecurrent, over(agecat)
*Region
svy: mean minwagecurrent, over(region)
*Employment
svy: mean minwagecurrent, over(employ)

*MINIMUM WAGE PREFERRED

*AVERAGE
svy: mean minwagepref

*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean minwagepref, over(gender)
*Race
svy: mean minwagepref, over(race)
*Party ID
svy: mean minwagepref, over(pid3)
*Ideology
svy: mean minwagepref, over(ideo3)
*Income
svy: mean minwagepref, over(incomecat)
*Age Category
svy: mean minwagepref, over(agecat)
*Region
svy: mean minwagepref, over(region)
*Employment
svy: mean minwagepref, over(employ)

* IMMIGRATION - ILLEGAL 2012

*AVERAGE
svy: mean Q5

*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean Q5, over(gender)
*Race
svy: mean Q5, over(race)
*Party ID
svy: mean Q5, over(pid3)
*Ideology
svy: mean Q5, over(ideo3)
*Income
svy: mean Q5, over(incomecat)
*Age Category
svy: mean Q5, over(agecat)
*Region
svy: mean Q5, over(region)
*Border states
svy: mean Q5, over(borderstate)

* IMMIGRATION - LEGAL 2012

*AVERAGE
svy: mean Q4

*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean Q4, over(gender)
*Race
svy: mean Q4, over(race)
*Party ID
svy: mean Q4, over(pid3)
*Ideology
svy: mean Q4, over(ideo3)

*Income
svy: mean Q4, over(incomecat)
*Region
svy: mean Q4, over(region)
*Age Category
svy: mean Q4, over(agecat)
*Border states
svy: mean Q4, over(borderstate)

* IMMIGRATION - LEGAL ANNUAL MAX

*AVERAGE
svy: mean Q6

*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean Q6, over(gender)
*Race
svy: mean Q6, over(race)
*Party ID
svy: mean Q6, over(pid3)
*Ideology
svy: mean Q6, over(ideo3)
*Region
svy: mean Q6, over(region)
*Income
svy: mean Q6, over(incomecat)
*Age Category
svy: mean Q6, over(agecat)
*Border states
svy: mean Q6, over(borderstate)

* RETIREMENT AGE ACROSS DIFFERENT PROFESSIONS

* Construction
svy: mean Q11_1_1
*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean Q11_1_1, over(gender)
*Race
svy: mean Q11_1_1, over(race)
*Party ID
svy: mean Q11_1_1, over(pid3)
*Ideology
svy: mean Q11_1_1, over(ideo3)
*Income
svy: mean Q11_1_1, over(incomecat)
*Age Category
svy: mean Q11_1_1, over(agecat)

* Retail/Sales
svy: mean Q11_2_1
*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean Q11_2_1, over(gender)
*Race
svy: mean Q11_2_1, over(race)
*Party ID
svy: mean Q11_2_1, over(pid3)
*Ideology
svy: mean Q11_2_1, over(ideo3)
*Income
svy: mean Q11_2_1, over(incomecat)
*Age Category
svy: mean Q11_2_1, over(agecat)

* Office clerk
svy: mean Q11_3_1
*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean Q11_3_1, over(gender)
*Race
svy: mean Q11_3_1, over(race)
*Party ID
svy: mean Q11_3_1, over(pid3)
*Ideology
svy: mean Q11_3_1, over(ideo3)
*Income
svy: mean Q11_3_1, over(incomecat)
*Age Category
svy: mean Q11_3_1, over(agecat)

* Teaching
svy: mean Q11_4_1
*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean Q11_4_1, over(gender)
*Race
svy: mean Q11_4_1, over(race)
*Party ID
svy: mean Q11_4_1, over(pid3)
*Ideology
svy: mean Q11_4_1, over(ideo3)
*Income
svy: mean Q11_4_1, over(incomecat)
*Age Category
svy: mean Q11_4_1, over(agecat)

* Food service
svy: mean Q11_5_1
*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean Q11_5_1, over(gender)
*Race
svy: mean Q11_5_1, over(race)
*Party ID
svy: mean Q11_5_1, over(pid3)
*Ideology
svy: mean Q11_5_1, over(ideo3)
*Income
svy: mean Q11_5_1, over(incomecat)
*Age Category
svy: mean Q11_5_1, over(agecat)

* Nurses
svy: mean Q11_6_1
*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean Q11_6_1, over(gender)
*Race
svy: mean Q11_6_1, over(race)
*Party ID
svy: mean Q11_6_1, over(pid3)
*Ideology
svy: mean Q11_6_1, over(ideo3)
*Income
svy: mean Q11_6_1, over(incomecat)
*Age Category
svy: mean Q11_6_1, over(agecat)

* Executives and managers
svy: mean Q11_7_1
*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean Q11_7_1, over(gender)
*Race
svy: mean Q11_7_1, over(race)
*Party ID
svy: mean Q11_7_1, over(pid3)
*Ideology
svy: mean Q11_7_1, over(ideo3)
*Income
svy: mean Q11_7_1, over(incomecat)
*Age Category
svy: mean Q11_7_1, over(agecat)

* SOTU ISSUES
*Economy/Jobs
svy: tab Q1_1
svy: tab Q1_1 gender, col stubw(50)
svy: tab Q1_1 pid3, col stubw(50)
svy: tab Q1_1 ideo3, col stubw(50)
svy: tab Q1_1 race, col stubw(50)
svy: tab Q1_1 agecat, col stubw(50)
svy: tab Q1_1 incomecat, col stubw(50)

*National debt/deficit
svy: tab Q1_2
svy: tab Q1_2 gender, col stubw(50)
svy: tab Q1_2 pid3, col stubw(50)
svy: tab Q1_2 ideo3, col stubw(50)
svy: tab Q1_2 race, col stubw(50)
svy: tab Q1_2 agecat, col stubw(50)
svy: tab Q1_2 incomecat, col stubw(50)

*National Security
svy: tab Q1_3
svy: tab Q1_3 gender, col stubw(50)
svy: tab Q1_3 pid3, col stubw(50)
svy: tab Q1_3 ideo3, col stubw(50)
svy: tab Q1_3 race, col stubw(50)
svy: tab Q1_3 agecat, col stubw(50)
svy: tab Q1_3 incomecat, col stubw(50)

*Energy
svy: tab Q1_4
svy: tab Q1_4 gender, col stubw(50)
svy: tab Q1_4 pid3, col stubw(50)
svy: tab Q1_4 ideo3, col stubw(50)
svy: tab Q1_4 race, col stubw(50)
svy: tab Q1_4 agecat, col stubw(50)
svy: tab Q1_4 incomecat, col stubw(50)

*Education
svy: tab Q1_5
svy: tab Q1_5 gender, col stubw(50)
svy: tab Q1_5 pid3, col stubw(50)
svy: tab Q1_5 ideo3, col stubw(50)
svy: tab Q1_5 race, col stubw(50)
svy: tab Q1_5 agecat, col stubw(50)
svy: tab Q1_5 incomecat, col stubw(50)

*Climate change
svy: tab Q1_6
svy: tab Q1_6 gender, col stubw(50)
svy: tab Q1_6 pid3, col stubw(50)
svy: tab Q1_6 ideo3, col stubw(50)
svy: tab Q1_6 race, col stubw(50)
svy: tab Q1_6 agecat, col stubw(50)
svy: tab Q1_6 incomecat, col stubw(50)

*Voting Rights
svy: tab Q1_7
svy: tab Q1_7 gender, col stubw(50)
svy: tab Q1_7 pid3, col stubw(50)
svy: tab Q1_7 ideo3, col stubw(50)
svy: tab Q1_7 race, col stubw(50)
svy: tab Q1_7 agecat, col stubw(50)
svy: tab Q1_7 incomecat, col stubw(50)

*Healthcare
svy: tab Q1_8
svy: tab Q1_8 gender, col stubw(50)
svy: tab Q1_8 pid3, col stubw(50)
svy: tab Q1_8 ideo3, col stubw(50)
svy: tab Q1_8 race, col stubw(50)
svy: tab Q1_8 agecat, col stubw(50)
svy: tab Q1_8 incomecat, col stubw(50)

*Immigration
svy: tab Q1_9
svy: tab Q1_9 gender, col stubw(50)
svy: tab Q1_9 pid3, col stubw(50)
svy: tab Q1_9 ideo3, col stubw(50)
svy: tab Q1_9 race, col stubw(50)
svy: tab Q1_9 agecat, col stubw(50)
svy: tab Q1_9 incomecat, col stubw(50)

*Gun Control
svy: tab Q1_10
svy: tab Q1_10 gender, col stubw(50)
svy: tab Q1_10 pid3, col stubw(50)
svy: tab Q1_10 ideo3, col stubw(50)
svy: tab Q1_10 race, col stubw(50)
svy: tab Q1_10 agecat, col stubw(50)
svy: tab Q1_10 incomecat, col stubw(50)


* IMMIGRATION - SKILLED WORKERS

*science, engineering, technology
svy: tab Q7_a
svy: tab Q7_a gender, col stubw(50)
svy: tab Q7_a pid3, col stubw(50)
svy: tab Q7_a ideo3, col stubw(50)
svy: tab Q7_a race, col stubw(50)
svy: tab Q7_a agecat, col stubw(50)
svy: tab Q7_a incomecat, col stubw(50)

*construction, farming, landscaping
svy: tab Q7_b
svy: tab Q7_b gender, col stubw(50)
svy: tab Q7_b pid3, col stubw(50)
svy: tab Q7_b ideo3, col stubw(50)
svy: tab Q7_b race, col stubw(50)
svy: tab Q7_b agecat, col stubw(50)
svy: tab Q7_b incomecat, col stubw(50)

*doctors, nurses
svy: tab Q7_c
svy: tab Q7_c gender, col stubw(50)
svy: tab Q7_c pid3, col stubw(50)
svy: tab Q7_c ideo3, col stubw(50)
svy: tab Q7_c race, col stubw(50)
svy: tab Q7_c agecat, col stubw(50)
svy: tab Q7_c incomecat, col stubw(50)

*hospitality, restaurant, domestic
svy: tab Q7_d
svy: tab Q7_d gender, col stubw(50)
svy: tab Q7_d pid3, col stubw(50)
svy: tab Q7_d ideo3, col stubw(50)
svy: tab Q7_d race, col stubw(50)
svy: tab Q7_d agecat, col stubw(50)
svy: tab Q7_d incomecat, col stubw(50)

*students
svy: tab Q7_e
svy: tab Q7_e gender, col stubw(50)
svy: tab Q7_e pid3, col stubw(50)
svy: tab Q7_e ideo3, col stubw(50)
svy: tab Q7_e race, col stubw(50)
svy: tab Q7_e agecat, col stubw(50)
svy: tab Q7_e incomecat, col stubw(50)

* BLAME

* Parties
*AVERAGE
svy: mean Q19

*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean Q19, over(gender)
*Race
svy: mean Q19, over(race)
*Party ID
svy: mean Q19, over(pid3)
*Ideology
svy: mean Q19, over(ideo3)
*Income
svy: mean Q19, over(incomecat)
*Age Category
svy: mean Q19, over(agecat)
*Region
svy: mean Q19, over(region)
*Employment
svy: mean Q19, over(employ)

* Congress/Pres
*AVERAGE
svy: mean Q20

*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean Q20, over(gender)
*Race
svy: mean Q20, over(race)
*Party ID
svy: mean Q20, over(pid3)
*Ideology
svy: mean Q20, over(ideo3)
*Income
svy: mean Q20, over(incomecat)
*Age Category
svy: mean Q20, over(agecat)
*Region
svy: mean Q20, over(region)
*Employment
svy: mean Q20, over(employ)

* Boehner/Obama

*AVERAGE
svy: mean Q20b

*AVERAGE FOR DEMO GROUPS
*Gender
svy: mean Q20b, over(gender)
*Race
svy: mean Q20b, over(race)
*Party ID
svy: mean Q20b, over(pid3)
*Ideology
svy: mean Q20b, over(ideo3)
*Income
svy: mean Q20b, over(incomecat)
*Age Category
svy: mean Q20b, over(agecat)
*Region
svy: mean Q20b, over(region)
*Employment
svy: mean Q20b, over(employ)


*DONE


